Main

In your final repo, there should be an R markdown file that organizes all computational steps for evaluating your proposed Facial Expression Recognition framework.

This file is currently a template for running evaluation experiments. You should update it according to your codes but following precisely the same structure.

if(!require("EBImage")){
  install.packages("BiocManager")
  BiocManager::install("EBImage")
}
if(!require("R.matlab")){
  install.packages("R.matlab")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("dplyr")){
  install.packages("dplyr")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("ggplot2")){
  install.packages("ggplot2")
}

if(!require("caret")){
  install.packages("caret")
}

if(!require("glmnet")){
  install.packages("glmnet")
}

if(!require("WeightedROC")){
  install.packages("WeightedROC")
}

if(!require("gbm")){
  install.packages("gbm")
}

if(!require("xgboost")){
  install.packages("xgboost")
}

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if(!require("caret")){
  install.packages("caret")
}

# Install Miniconda (https://docs.conda.io/en/latest/miniconda.html)
if(!require("keras")){
  install.packages("keras")
}

if(!require("tensorflow")){
  install.packages("tensorflow")
  install_tensorflow()
}

use_condaenv("r-tensorflow")
library(keras)
library(tensorflow)

library(R.matlab)
library(readxl)
library(dplyr)
library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
require(xgboost)
library(caret)

Step 0 set work directories

set.seed(2040)
# setwd("~/Project3-FacialEmotionRecognition/doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located. 
# use relative path for reproducibility

Provide directories for training images. Training images and Training fiducial points will be in different subfolders.

train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir,  "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="") 

Step 1: set up controls for evaluation experiments.

In this chunk, we have a set of controls for the evaluation experiments.

• (T/F) cross-validation on the training set
• (T/F) reweighting the samples for training set
• (number) K, the number of CV folds
• (T/F) process features for training set
• (T/F) run evaluation on an independent test set
• (T/F) process features for test set

# run.cv <- TRUE # run cross-validation on the training set
# sample.reweight <- TRUE # run sample reweighting in model training
# K <- 5  # number of CV folds
# run.feature.train <- TRUE # process features for training set
# run.test <- TRUE # run evaluation on an independent test set
# run.feature.test <- TRUE # process features for test set

sample.reweight <- TRUE # run sample reweighting in model training
K <- 5  # number of CV folds
run.feature.train <- TRUE # process features for training set
run.feature.test <- TRUE # process features for test set

run.cv_gbm <- TRUE # run GBM cross-validation on the training set
run.test_gbm <- TRUE # run GBM evaluation on an independent test set

run.cv_xgboost <- TRUE # run DNN cross-validation on the training set
run.test_xgboost <- TRUE # run DNN evaluation on an independent test set

run.cv_dnn <- TRUE # run DNN cross-validation on the training set
run.test_dnn <- TRUE # run DNN evaluation on an independent test set

Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this part, we tune parameter n.trees and shrinkage for GBM.

# GBM parameters
n.trees <- c(500, 100, 1500)
shrinkage <- c(0.01, 0.05, 0.1)

# XGBoost parameters
eta <- c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
colsample_bytree <- c(0.2, 0.5, 0.6, 0.7, 0.8, 0.9)

Step 2: import data and train-test split

#train-test split
info <- read.csv(train_label_path)
n <- nrow(info)
n_train <- round(n*(4/5), 0)
train_idx <- sample(info$Index, n_train, replace = F)
test_idx <- setdiff(info$Index, train_idx)

If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them.

n_files <- length(list.files(train_image_dir))

image_list <- list()
for(i in 1:100){
   image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
}

Fiducial points are stored in matlab format. In this step, we read them and store them in a list.

#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
     return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}

#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)

strings not representable in native encoding will be translated to UTF-8

save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")

Step 3: construct features and responses

• The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.

  • In the first column, 78 fiducials points of each emotion are marked in order.
  • In the second column distributions of vertical distance between right pupil(1) and right brow peak(21) are shown in histograms. For example, the distance of an angry face tends to be shorter than that of a surprised face.
  • The third column is the distributions of vertical distances between right mouth corner(50) and the midpoint of the upper lip(52). For example, the distance of an happy face tends to be shorter than that of a sad face.

Figure1

feature.R should be the wrapper for all your feature engineering functions and options. The function feature( ) should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later.

• feature.R
• Input: list of images or fiducial point
• Output: an RData file that contains extracted features and corresponding responses

source("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
  tm_feature_train <- system.time(dat_train <- feature(fiducial_pt_list, train_idx))
  save(dat_train, file="../output/feature_train.RData")
}else{
  load(file="../output/feature_train.RData")
}

tm_feature_test <- NA
if(run.feature.test){
  tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx))
  save(dat_test, file="../output/feature_test.RData")
}else{
  load(file="../output/feature_test.RData")
}

# # PCA of features
# source("../lib/feature_pca.R")
# 
# if(TRUE){
#   tm_feature_train_pca <- system.time({feature_train_pca = as.matrix(feature_pca(dat_train));
#   feature_test_pca = as.matrix(feature_pca(dat_test))
#   })
#   save(feature_train_pca, file="../output/feature_train_pca.RData")
#   save(feature_test_pca, file="../output/feature_test_pca.RData")
# }else{
#   load(file="../output/feature_train_pca.RData")
#   load(file="../output/feature_test_pca.RData")
# }

GBM

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

• train.R
  • Input: a data frame containing features and labels and a parameter list.
  • Output:a trained model
• test.R
  • Input: the fitted classification model using training data and processed features from testing images
  • Input: an R object that contains a trained classifier.
  • Output: training model specification
• In this part, we use GBM (baseline model) to do classification.

source("../lib/train_gbm.R") 
source("../lib/test_gbm.R")

Model selection with cross-validation

• Do model selection by choosing among different values of training model parameters.

Visualize cross-validation results.

res_cv_gbm <- as.data.frame(res_cv_gbm) 

if(run.cv_gbm){
  p1 <- res_cv_gbm %>% 
    ggplot(aes(x = n.trees, y = mean_error,
               ymin = mean_error - sd_error, ymax = mean_error + sd_error)) + 
    geom_crossbar() +
    facet_wrap(~shrinkage)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  p2 <- res_cv_gbm %>% 
    ggplot(aes(x = n.trees, y = mean_AUC,
               ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) + 
    geom_crossbar() +
    facet_wrap(~shrinkage)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  print(p1)
  print(p2)
}

• Choose the “best” parameter value

# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])

par_n.trees_best <- 500
par_shrinkage_best <- 0.05

• Train the model with the entire training set using the selected model (model parameter) via cross-validation.

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

tm_train <- NA
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = weight_train, par_n.trees_best, par_shrinkage_best))
} else {
  tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = NULL, par_n.trees_best, par_shrinkage_best))
}
save(fit_train, file="../output/fit_train_gbm.RData")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_gbm){
  load(file="../output/fit_train_gbm.RData")
  tm_test <- system.time({prob_pred <- test_gbm(fit_train, feature_test, par_n.trees_best, pred.type = 'response');})
}

• evaluation

## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)

# DELETE
save(feature_test, file="../output/feature_test_tmp_only.RData")
save(label_test, file="../output/label_test_tmp_only.RData")
# DELETE

weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 1, 0)

accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)


cat("The accuracy of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", accu*100, "%.\n")

The accuracy of model: GBM with n.trees = 500 and shrinkage = 0.05 is 71.24604 %.

cat("The AUC of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", auc, ".\n")

The AUC of model: GBM with n.trees = 500 and shrinkage = 0.05 is 0.7879471 .

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n") 
cat("Time for testing model=", tm_test[3], "s \n")

XGBoost

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

• train.R
  • Input: a data frame containing features and labels and a parameter list.
  • Output:a trained model
• test.R
  • Input: the fitted classification model using training data and processed features from testing images
  • Input: an R object that contains a trained classifier.
  • Output: training model specification
• In this part, we use GBM (baseline model) to do classification.

source("../lib/train_xgboost.R") 
source("../lib/test_xgboost.R")

Model selection with cross-validation

• Do model selection by choosing among different values of training model parameters.

source("../lib/cross_validation_xgboost.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)

weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

if(run.cv_xgboost){
  res_cv_xgboost <- matrix(0, nrow = length(eta) * length(colsample_bytree), ncol = 6)
  count = 0
  for(i in 1:length(eta)){
    for(j in 1:length(colsample_bytree)){
      count = count + 1
      cat("eta =", eta[i], "\n")
      cat("colsample_bytree =", colsample_bytree[j], "\n")
      
      res_cv <- cv.function_xgboost(feature_train, label_train, K
                                    , eta[i], colsample_bytree[j], reweight = sample.reweight)
      
      res_cv_xgboost[count,] <- c(eta[i], colsample_bytree[j], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
    }
  }
  
  colnames(res_cv_xgboost) <- c("eta","colsample_bytree","mean_error", "sd_error", "mean_AUC", "sd_AUC")
  save(res_cv_xgboost, file="../output/res_cv_xgboost.RData")
}else{
  load("../output/res_cv_xgboost.RData")
}

Visualize cross-validation results.

res_cv_xgboost <- as.data.frame(res_cv_xgboost) 

if(run.cv_xgboost){
  p1 <- res_cv_xgboost %>% 
    ggplot(aes(x = eta, y = mean_error,
               ymin = mean_error - sd_error, ymax = mean_error + sd_error)) + 
    geom_crossbar() +
    facet_wrap(~colsample_bytree)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  p2 <- res_cv_xgboost %>% 
    ggplot(aes(x = eta, y = mean_AUC,
               ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) + 
    geom_crossbar() +
    facet_wrap(~colsample_bytree)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  print(p1)
  print(p2)
}

• Choose the “best” parameter value

# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])

# par_eta_best <- 0.1
# par_colsample_bytree_best <- 0.6

# par_eta_best <- 0.05
# par_colsample_bytree_best <- 0.2

# eta <- c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5)
# colsample_bytree <- c(0.2, 0.5, 0.6, 0.7, 0.8, 0.9)
par_eta_best <- 0.3
par_colsample_bytree_best <- 0.7

• Train the model with the entire training set using the selected model (model parameter) via cross-validation.

source("../lib/train_xgboost.R") 
source("../lib/test_xgboost.R")

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

tm_train <- NA
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_xgboost(feature_train, label_train, par_eta_best, par_colsample_bytree_best, sample.reweight))
} else {
  tm_train <- system.time(fit_train <- train_xgboost(feature_train, label_train, par_eta_best, par_colsample_bytree_best, NULL))
}
xgb.save(fit_train, "../output/fit_train_xgboost.model")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_xgboost){
  fit_train <- xgb.load("../output/fit_train_xgboost.model")
  tm_test <- system.time({prob_pred <- test_xgboost(fit_train, feature_test, pred.type = 'probability');})
}

• evaluation

## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)

weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 1, 0)

accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)


cat("The accuracy of model:", "XGBoost with eta =" , par_eta_best, "and colsample_bytree =", par_colsample_bytree_best, "is", accu*100, "%.\n")
cat("The AUC of model:", "XGBoost with eta =" , par_eta_best, "and colsample_bytree =", par_colsample_bytree_best, "is", auc, ".\n")

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n") 
cat("Time for testing model=", tm_test[3], "s \n")

DNN

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

• train.R
  • Input: a data frame containing features and labels and a parameter list.
  • Output:a trained model
• test.R
  • Input: the fitted classification model using training data and processed features from testing images
  • Input: an R object that contains a trained classifier.
  • Output: training model specification
• In this part, we use GBM (baseline model) to do classification.

source("../lib/train_dnn.R") 
source("../lib/test_dnn.R")

Model selection with cross-validation

• Do model selection by choosing among different values of training model parameters.

source("../lib/cross_validation_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)

# if(run.cv_dnn){
#   res_cv_dnn <- matrix(0, 1, ncol = 4)
#   
#   res_cv <- cv.function_dnn(features = feature_train, labels = label_train, K,
#                           reweight = sample.reweight)
#   
#   res_cv_dnn[1,] <- c(res_cv[1], res_cv[2], res_cv[3], res_cv[4])
#   
#   colnames(res_cv_dnn) <- c("mean_error", "sd_error", "mean_AUC", "sd_AUC")
#   save(res_cv_dnn, file="../output/res_cv_dnn.RData")
# }else{
#   load("../output/res_cv_dnn.RData")
# }

Visualize cross-validation results.

# res_cv_dnn <- as.data.frame(res_cv_dnn) 
# 
# if(run.cv_dnn){
#   p1 <- res_cv_dnn %>% 
#     ggplot(aes(x = "DNN", y = mean_error,
#                ymin = mean_error - sd_error, ymax = mean_error + sd_error)) + 
#     geom_crossbar() +
#     theme(axis.text.x = element_text())
#   
#   p2 <- res_cv_dnn %>% 
#     ggplot(aes(x = "DNN", y = mean_AUC,
#                ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) + 
#     geom_crossbar() +
#     theme(axis.text.x = element_text())
#   
#   print(p1)
#   print(p2)
# }

• Choose the “best” parameter value

# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])

• Train the model with the entire training set using the selected model (model parameter) via cross-validation.

source("../lib/train_dnn.R") 
source("../lib/test_dnn.R")

feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
feature_test <- as.matrix(dat_test[, -6007])
label_test <- as.integer(dat_test$label)
label_test <- ifelse(label_test == 2, 1, 0)
weight_test <- rep(NA, length(label_test))

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

# validation_split <- 1 / 5
# validation_split <- 0
tm_train <- NA
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = weight_train, feature_test, label_test, weight_test))
} else {
  tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = NULL, feature_test, label_test, weight_test = NULL))
}

Epoch 1/100

 1/48 [..............................] - ETA: 0s - loss: 1.9691 - accuracy: 0.3200
 3/48 [>.............................] - ETA: 0s - loss: 1.9725 - accuracy: 0.3133
 5/48 [==>...........................] - ETA: 1s - loss: 1.8977 - accuracy: 0.3360
 7/48 [===>..........................] - ETA: 1s - loss: 1.8592 - accuracy: 0.3714
 9/48 [====>.........................] - ETA: 1s - loss: 1.8618 - accuracy: 0.3933
11/48 [=====>........................] - ETA: 1s - loss: 1.8598 - accuracy: 0.3964
13/48 [=======>......................] - ETA: 1s - loss: 1.8827 - accuracy: 0.4046
15/48 [========>.....................] - ETA: 1s - loss: 1.8787 - accuracy: 0.4107
17/48 [=========>....................] - ETA: 0s - loss: 1.8691 - accuracy: 0.4224
19/48 [==========>...................] - ETA: 0s - loss: 1.8643 - accuracy: 0.4253
21/48 [============>.................] - ETA: 0s - loss: 1.8531 - accuracy: 0.4238
23/48 [=============>................] - ETA: 0s - loss: 1.8482 - accuracy: 0.4235
25/48 [==============>...............] - ETA: 0s - loss: 1.8572 - accuracy: 0.4272
27/48 [===============>..............] - ETA: 0s - loss: 1.8495 - accuracy: 0.4274
29/48 [=================>............] - ETA: 0s - loss: 1.8400 - accuracy: 0.4359
31/48 [==================>...........] - ETA: 0s - loss: 1.8365 - accuracy: 0.4381
33/48 [===================>..........] - ETA: 0s - loss: 1.8434 - accuracy: 0.4394
35/48 [====================>.........] - ETA: 0s - loss: 1.8404 - accuracy: 0.4400
37/48 [======================>.......] - ETA: 0s - loss: 1.8365 - accuracy: 0.4449
39/48 [=======================>......] - ETA: 0s - loss: 1.8355 - accuracy: 0.4518
41/48 [========================>.....] - ETA: 0s - loss: 1.8286 - accuracy: 0.4566
43/48 [=========================>....] - ETA: 0s - loss: 1.8292 - accuracy: 0.4567
45/48 [===========================>..] - ETA: 0s - loss: 1.8323 - accuracy: 0.4613
47/48 [============================>.] - ETA: 0s - loss: 1.8291 - accuracy: 0.4655
48/48 [==============================] - 2s 33ms/step - loss: 1.8296 - accuracy: 0.4650

48/48 [==============================] - 2s 50ms/step - loss: 1.8296 - accuracy: 0.4650 - val_loss: 1.7043 - val_accuracy: 0.6833
Epoch 2/100

 1/48 [..............................] - ETA: 0s - loss: 1.6416 - accuracy: 0.5200
 3/48 [>.............................] - ETA: 0s - loss: 1.7606 - accuracy: 0.5067
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48/48 [==============================] - 2s 33ms/step - loss: 1.7345 - accuracy: 0.5583

48/48 [==============================] - 2s 38ms/step - loss: 1.7345 - accuracy: 0.5583 - val_loss: 1.6564 - val_accuracy: 0.6717
Epoch 3/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.6834 - accuracy: 0.6121 - val_loss: 1.6197 - val_accuracy: 0.6633
Epoch 4/100

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48/48 [==============================] - 2s 39ms/step - loss: 1.6469 - accuracy: 0.6346 - val_loss: 1.5948 - val_accuracy: 0.7217
Epoch 5/100

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48/48 [==============================] - 2s 33ms/step - loss: 1.6242 - accuracy: 0.6675

48/48 [==============================] - 2s 39ms/step - loss: 1.6242 - accuracy: 0.6675 - val_loss: 1.5798 - val_accuracy: 0.6933
Epoch 6/100

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48/48 [==============================] - 2s 33ms/step - loss: 1.6055 - accuracy: 0.6746

48/48 [==============================] - 2s 39ms/step - loss: 1.6055 - accuracy: 0.6746 - val_loss: 1.5461 - val_accuracy: 0.7050
Epoch 7/100

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48/48 [==============================] - 2s 36ms/step - loss: 1.5660 - accuracy: 0.6975

48/48 [==============================] - 2s 42ms/step - loss: 1.5660 - accuracy: 0.6975 - val_loss: 1.5521 - val_accuracy: 0.7467
Epoch 8/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.5425 - accuracy: 0.7167

48/48 [==============================] - 2s 44ms/step - loss: 1.5425 - accuracy: 0.7167 - val_loss: 1.5368 - val_accuracy: 0.7167
Epoch 9/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.5252 - accuracy: 0.7092

48/48 [==============================] - 2s 44ms/step - loss: 1.5252 - accuracy: 0.7092 - val_loss: 1.5197 - val_accuracy: 0.7317
Epoch 10/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.4983 - accuracy: 0.7225

48/48 [==============================] - 2s 44ms/step - loss: 1.4983 - accuracy: 0.7225 - val_loss: 1.4933 - val_accuracy: 0.7217
Epoch 11/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.4897 - accuracy: 0.7421

48/48 [==============================] - 2s 44ms/step - loss: 1.4897 - accuracy: 0.7421 - val_loss: 1.4799 - val_accuracy: 0.7717
Epoch 12/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.4862 - accuracy: 0.7304

48/48 [==============================] - 2s 44ms/step - loss: 1.4862 - accuracy: 0.7304 - val_loss: 1.5134 - val_accuracy: 0.7050
Epoch 13/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.4485 - accuracy: 0.7433

48/48 [==============================] - 2s 44ms/step - loss: 1.4485 - accuracy: 0.7433 - val_loss: 1.4495 - val_accuracy: 0.7483
Epoch 14/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.4501 - accuracy: 0.7375

48/48 [==============================] - 2s 44ms/step - loss: 1.4501 - accuracy: 0.7375 - val_loss: 1.4278 - val_accuracy: 0.7567
Epoch 15/100

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48/48 [==============================] - 2s 43ms/step - loss: 1.4129 - accuracy: 0.7513 - val_loss: 1.4304 - val_accuracy: 0.7400
Epoch 16/100

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48/48 [==============================] - 2s 43ms/step - loss: 1.4200 - accuracy: 0.7496 - val_loss: 1.4270 - val_accuracy: 0.7733
Epoch 17/100

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48/48 [==============================] - 2s 44ms/step - loss: 1.4068 - accuracy: 0.7546 - val_loss: 1.4173 - val_accuracy: 0.7550
Epoch 18/100

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48/48 [==============================] - 2s 44ms/step - loss: 1.3848 - accuracy: 0.7667 - val_loss: 1.4076 - val_accuracy: 0.7017
Epoch 19/100

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48/48 [==============================] - 2s 44ms/step - loss: 1.3527 - accuracy: 0.7658 - val_loss: 1.3857 - val_accuracy: 0.7500
Epoch 20/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.3480 - accuracy: 0.7783

48/48 [==============================] - 2s 44ms/step - loss: 1.3480 - accuracy: 0.7783 - val_loss: 1.4350 - val_accuracy: 0.7283
Epoch 21/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.3240 - accuracy: 0.7783

48/48 [==============================] - 2s 44ms/step - loss: 1.3240 - accuracy: 0.7783 - val_loss: 1.3714 - val_accuracy: 0.7817
Epoch 22/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.3000 - accuracy: 0.7954

48/48 [==============================] - 2s 44ms/step - loss: 1.3000 - accuracy: 0.7954 - val_loss: 1.3989 - val_accuracy: 0.7550
Epoch 23/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.2966 - accuracy: 0.7962

48/48 [==============================] - 2s 43ms/step - loss: 1.2966 - accuracy: 0.7962 - val_loss: 1.3428 - val_accuracy: 0.8083
Epoch 24/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.2804 - accuracy: 0.7850

48/48 [==============================] - 2s 44ms/step - loss: 1.2804 - accuracy: 0.7850 - val_loss: 1.3808 - val_accuracy: 0.7717
Epoch 25/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.2746 - accuracy: 0.7971

48/48 [==============================] - 2s 44ms/step - loss: 1.2746 - accuracy: 0.7971 - val_loss: 1.3547 - val_accuracy: 0.7783
Epoch 26/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.2312 - accuracy: 0.8117

48/48 [==============================] - 2s 45ms/step - loss: 1.2312 - accuracy: 0.8117 - val_loss: 1.3480 - val_accuracy: 0.7950
Epoch 27/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.2284 - accuracy: 0.8108

48/48 [==============================] - 2s 43ms/step - loss: 1.2284 - accuracy: 0.8108 - val_loss: 1.3284 - val_accuracy: 0.7983
Epoch 28/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.2067 - accuracy: 0.8158

48/48 [==============================] - 2s 44ms/step - loss: 1.2067 - accuracy: 0.8158 - val_loss: 1.3604 - val_accuracy: 0.8050
Epoch 29/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.2155 - accuracy: 0.8104

48/48 [==============================] - 2s 43ms/step - loss: 1.2155 - accuracy: 0.8104 - val_loss: 1.3412 - val_accuracy: 0.8217
Epoch 30/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.1832 - accuracy: 0.8167

48/48 [==============================] - 2s 43ms/step - loss: 1.1832 - accuracy: 0.8167 - val_loss: 1.3361 - val_accuracy: 0.8150
Epoch 31/100

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48/48 [==============================] - 2s 44ms/step - loss: 1.1767 - accuracy: 0.8067 - val_loss: 1.3106 - val_accuracy: 0.8000
Epoch 32/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.1643 - accuracy: 0.8200

48/48 [==============================] - 2s 44ms/step - loss: 1.1643 - accuracy: 0.8200 - val_loss: 1.3046 - val_accuracy: 0.7983
Epoch 33/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.1572 - accuracy: 0.8204

48/48 [==============================] - 2s 44ms/step - loss: 1.1572 - accuracy: 0.8204 - val_loss: 1.2972 - val_accuracy: 0.7983
Epoch 34/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.1138 - accuracy: 0.8288

48/48 [==============================] - 2s 44ms/step - loss: 1.1138 - accuracy: 0.8288 - val_loss: 1.2904 - val_accuracy: 0.8100
Epoch 35/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.1309 - accuracy: 0.8283

48/48 [==============================] - 2s 43ms/step - loss: 1.1309 - accuracy: 0.8283 - val_loss: 1.2425 - val_accuracy: 0.8150
Epoch 36/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.1121 - accuracy: 0.8242

48/48 [==============================] - 2s 45ms/step - loss: 1.1121 - accuracy: 0.8242 - val_loss: 1.2423 - val_accuracy: 0.8100
Epoch 37/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.0655 - accuracy: 0.8375

48/48 [==============================] - 2s 44ms/step - loss: 1.0655 - accuracy: 0.8375 - val_loss: 1.2992 - val_accuracy: 0.8183
Epoch 38/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.0859 - accuracy: 0.8271

48/48 [==============================] - 2s 44ms/step - loss: 1.0859 - accuracy: 0.8271 - val_loss: 1.2781 - val_accuracy: 0.7817
Epoch 39/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.0498 - accuracy: 0.8425

48/48 [==============================] - 2s 44ms/step - loss: 1.0498 - accuracy: 0.8425 - val_loss: 1.2702 - val_accuracy: 0.8200
Epoch 40/100

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48/48 [==============================] - 2s 38ms/step - loss: 1.0366 - accuracy: 0.8504

48/48 [==============================] - 2s 44ms/step - loss: 1.0366 - accuracy: 0.8504 - val_loss: 1.2544 - val_accuracy: 0.7950
Epoch 41/100

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48/48 [==============================] - 2s 45ms/step - loss: 1.0356 - accuracy: 0.8404 - val_loss: 1.2352 - val_accuracy: 0.7983
Epoch 42/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.9884 - accuracy: 0.8683 - val_loss: 1.2488 - val_accuracy: 0.8167
Epoch 43/100

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48/48 [==============================] - 2s 37ms/step - loss: 0.9803 - accuracy: 0.8579

48/48 [==============================] - 2s 44ms/step - loss: 0.9803 - accuracy: 0.8579 - val_loss: 1.2973 - val_accuracy: 0.8033
Epoch 44/100

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48/48 [==============================] - 2s 37ms/step - loss: 1.0014 - accuracy: 0.8587

48/48 [==============================] - 2s 43ms/step - loss: 1.0014 - accuracy: 0.8587 - val_loss: 1.2702 - val_accuracy: 0.7767
Epoch 45/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.9960 - accuracy: 0.8650 - val_loss: 1.2273 - val_accuracy: 0.7917
Epoch 46/100

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48/48 [==============================] - 2s 37ms/step - loss: 0.9688 - accuracy: 0.8537

48/48 [==============================] - 2s 44ms/step - loss: 0.9688 - accuracy: 0.8537 - val_loss: 1.2365 - val_accuracy: 0.7967
Epoch 47/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.9365 - accuracy: 0.8737

48/48 [==============================] - 2s 44ms/step - loss: 0.9365 - accuracy: 0.8737 - val_loss: 1.2343 - val_accuracy: 0.8100
Epoch 48/100

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48/48 [==============================] - 2s 37ms/step - loss: 0.9241 - accuracy: 0.8683

48/48 [==============================] - 2s 44ms/step - loss: 0.9241 - accuracy: 0.8683 - val_loss: 1.3262 - val_accuracy: 0.8283
Epoch 49/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.9705 - accuracy: 0.8529

48/48 [==============================] - 2s 44ms/step - loss: 0.9705 - accuracy: 0.8529 - val_loss: 1.1856 - val_accuracy: 0.7867
Epoch 50/100

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48/48 [==============================] - 2s 37ms/step - loss: 0.9225 - accuracy: 0.8533

48/48 [==============================] - 2s 44ms/step - loss: 0.9225 - accuracy: 0.8533 - val_loss: 1.2344 - val_accuracy: 0.7867
Epoch 51/100

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48/48 [==============================] - 2s 43ms/step - loss: 0.9180 - accuracy: 0.8562 - val_loss: 1.1941 - val_accuracy: 0.8183
Epoch 52/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.8954 - accuracy: 0.8683 - val_loss: 1.1645 - val_accuracy: 0.8000
Epoch 53/100

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48/48 [==============================] - 2s 43ms/step - loss: 0.9187 - accuracy: 0.8537 - val_loss: 1.2104 - val_accuracy: 0.7617
Epoch 54/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.8770 - accuracy: 0.8637 - val_loss: 1.1738 - val_accuracy: 0.7800
Epoch 55/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.8635 - accuracy: 0.8708

48/48 [==============================] - 2s 44ms/step - loss: 0.8635 - accuracy: 0.8708 - val_loss: 1.0848 - val_accuracy: 0.7967
Epoch 56/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.8447 - accuracy: 0.8779

48/48 [==============================] - 2s 44ms/step - loss: 0.8447 - accuracy: 0.8779 - val_loss: 1.1465 - val_accuracy: 0.7983
Epoch 57/100

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48/48 [==============================] - 2s 37ms/step - loss: 0.8338 - accuracy: 0.8763

48/48 [==============================] - 2s 43ms/step - loss: 0.8338 - accuracy: 0.8763 - val_loss: 1.1932 - val_accuracy: 0.8383
Epoch 58/100

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48/48 [==============================] - 2s 37ms/step - loss: 0.8207 - accuracy: 0.8942

48/48 [==============================] - 2s 43ms/step - loss: 0.8207 - accuracy: 0.8942 - val_loss: 1.1169 - val_accuracy: 0.8150
Epoch 59/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.8082 - accuracy: 0.8808

48/48 [==============================] - 2s 45ms/step - loss: 0.8082 - accuracy: 0.8808 - val_loss: 1.1488 - val_accuracy: 0.8367
Epoch 60/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.8195 - accuracy: 0.8829

48/48 [==============================] - 2s 44ms/step - loss: 0.8195 - accuracy: 0.8829 - val_loss: 1.1227 - val_accuracy: 0.8017
Epoch 61/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.8023 - accuracy: 0.8854 - val_loss: 1.1743 - val_accuracy: 0.8183
Epoch 62/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.7722 - accuracy: 0.8892 - val_loss: 1.1386 - val_accuracy: 0.8367
Epoch 63/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.7802 - accuracy: 0.8917

48/48 [==============================] - 2s 44ms/step - loss: 0.7802 - accuracy: 0.8917 - val_loss: 1.2621 - val_accuracy: 0.8150
Epoch 64/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.7872 - accuracy: 0.8804

48/48 [==============================] - 2s 44ms/step - loss: 0.7872 - accuracy: 0.8804 - val_loss: 1.1195 - val_accuracy: 0.8183
Epoch 65/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.7587 - accuracy: 0.8946 - val_loss: 1.1353 - val_accuracy: 0.7800
Epoch 66/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.7677 - accuracy: 0.8804

48/48 [==============================] - 2s 44ms/step - loss: 0.7677 - accuracy: 0.8804 - val_loss: 1.1352 - val_accuracy: 0.8267
Epoch 67/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.7324 - accuracy: 0.9021

48/48 [==============================] - 2s 45ms/step - loss: 0.7324 - accuracy: 0.9021 - val_loss: 1.1710 - val_accuracy: 0.7983
Epoch 68/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.7221 - accuracy: 0.8954

48/48 [==============================] - 2s 44ms/step - loss: 0.7221 - accuracy: 0.8954 - val_loss: 1.1456 - val_accuracy: 0.8000
Epoch 69/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.7235 - accuracy: 0.8850

48/48 [==============================] - 2s 45ms/step - loss: 0.7235 - accuracy: 0.8850 - val_loss: 1.2059 - val_accuracy: 0.8167
Epoch 70/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.7166 - accuracy: 0.8963

48/48 [==============================] - 2s 44ms/step - loss: 0.7166 - accuracy: 0.8963 - val_loss: 1.1929 - val_accuracy: 0.7817
Epoch 71/100

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48/48 [==============================] - 2s 43ms/step - loss: 0.7152 - accuracy: 0.9029 - val_loss: 1.1496 - val_accuracy: 0.7983
Epoch 72/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.7179 - accuracy: 0.8971 - val_loss: 1.1589 - val_accuracy: 0.7800
Epoch 73/100

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48/48 [==============================] - 2s 39ms/step - loss: 0.6974 - accuracy: 0.8971

48/48 [==============================] - 2s 45ms/step - loss: 0.6974 - accuracy: 0.8971 - val_loss: 1.1537 - val_accuracy: 0.8183
Epoch 74/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6962 - accuracy: 0.8896

48/48 [==============================] - 2s 45ms/step - loss: 0.6962 - accuracy: 0.8896 - val_loss: 1.1282 - val_accuracy: 0.8300
Epoch 75/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6954 - accuracy: 0.8971

48/48 [==============================] - 2s 45ms/step - loss: 0.6954 - accuracy: 0.8971 - val_loss: 1.1093 - val_accuracy: 0.7917
Epoch 76/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6766 - accuracy: 0.9083

48/48 [==============================] - 2s 44ms/step - loss: 0.6766 - accuracy: 0.9083 - val_loss: 1.1003 - val_accuracy: 0.8083
Epoch 77/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6721 - accuracy: 0.9054

48/48 [==============================] - 2s 44ms/step - loss: 0.6721 - accuracy: 0.9054 - val_loss: 1.1293 - val_accuracy: 0.8017
Epoch 78/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6562 - accuracy: 0.9158

48/48 [==============================] - 2s 44ms/step - loss: 0.6562 - accuracy: 0.9158 - val_loss: 1.1924 - val_accuracy: 0.8100
Epoch 79/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6596 - accuracy: 0.8983

48/48 [==============================] - 2s 45ms/step - loss: 0.6596 - accuracy: 0.8983 - val_loss: 1.2203 - val_accuracy: 0.8433
Epoch 80/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6821 - accuracy: 0.8975

48/48 [==============================] - 2s 44ms/step - loss: 0.6821 - accuracy: 0.8975 - val_loss: 1.0704 - val_accuracy: 0.8233
Epoch 81/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.6657 - accuracy: 0.9087 - val_loss: 1.1067 - val_accuracy: 0.7817
Epoch 82/100

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48/48 [==============================] - 2s 45ms/step - loss: 0.6661 - accuracy: 0.8946 - val_loss: 1.2199 - val_accuracy: 0.7950
Epoch 83/100

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48/48 [==============================] - 2s 45ms/step - loss: 0.6413 - accuracy: 0.9025 - val_loss: 1.1322 - val_accuracy: 0.7933
Epoch 84/100

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48/48 [==============================] - 2s 45ms/step - loss: 0.6400 - accuracy: 0.9133 - val_loss: 1.1503 - val_accuracy: 0.8150
Epoch 85/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.6232 - accuracy: 0.9150 - val_loss: 1.1511 - val_accuracy: 0.8183
Epoch 86/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.6490 - accuracy: 0.9013 - val_loss: 1.1314 - val_accuracy: 0.8000
Epoch 87/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6318 - accuracy: 0.9104

48/48 [==============================] - 2s 44ms/step - loss: 0.6318 - accuracy: 0.9104 - val_loss: 1.0846 - val_accuracy: 0.7867
Epoch 88/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6379 - accuracy: 0.8975

48/48 [==============================] - 2s 44ms/step - loss: 0.6379 - accuracy: 0.8975 - val_loss: 1.1112 - val_accuracy: 0.7983
Epoch 89/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.6087 - accuracy: 0.9133 - val_loss: 1.1846 - val_accuracy: 0.8050
Epoch 90/100

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48/48 [==============================] - 2s 37ms/step - loss: 0.6001 - accuracy: 0.9179

48/48 [==============================] - 2s 44ms/step - loss: 0.6001 - accuracy: 0.9179 - val_loss: 1.1631 - val_accuracy: 0.8233
Epoch 91/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.5999 - accuracy: 0.9117

48/48 [==============================] - 2s 44ms/step - loss: 0.5999 - accuracy: 0.9117 - val_loss: 1.1360 - val_accuracy: 0.8217
Epoch 92/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.6043 - accuracy: 0.9179

48/48 [==============================] - 2s 44ms/step - loss: 0.6043 - accuracy: 0.9179 - val_loss: 1.1409 - val_accuracy: 0.8267
Epoch 93/100

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48/48 [==============================] - 2s 45ms/step - loss: 0.5810 - accuracy: 0.9221 - val_loss: 1.0561 - val_accuracy: 0.8333
Epoch 94/100

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48/48 [==============================] - 2s 45ms/step - loss: 0.5980 - accuracy: 0.9196 - val_loss: 1.0835 - val_accuracy: 0.8233
Epoch 95/100

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48/48 [==============================] - 2s 44ms/step - loss: 0.6143 - accuracy: 0.9096 - val_loss: 1.0680 - val_accuracy: 0.7917
Epoch 96/100

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48/48 [==============================] - 2s 45ms/step - loss: 0.5947 - accuracy: 0.9175 - val_loss: 1.0798 - val_accuracy: 0.8150
Epoch 97/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.5747 - accuracy: 0.9237

48/48 [==============================] - 2s 45ms/step - loss: 0.5747 - accuracy: 0.9237 - val_loss: 1.1478 - val_accuracy: 0.7883
Epoch 98/100

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48/48 [==============================] - 2s 39ms/step - loss: 0.5813 - accuracy: 0.9250

48/48 [==============================] - 2s 45ms/step - loss: 0.5813 - accuracy: 0.9250 - val_loss: 1.0969 - val_accuracy: 0.7967
Epoch 99/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.5952 - accuracy: 0.9246

48/48 [==============================] - 2s 45ms/step - loss: 0.5952 - accuracy: 0.9246 - val_loss: 1.1217 - val_accuracy: 0.7800
Epoch 100/100

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48/48 [==============================] - 2s 38ms/step - loss: 0.5826 - accuracy: 0.9096

48/48 [==============================] - 2s 45ms/step - loss: 0.5826 - accuracy: 0.9096 - val_loss: 1.1541 - val_accuracy: 0.8083

save_model_hdf5(fit_train, "../output/fit_train_dnn.h5")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_dnn){
  fit_train <- load_model_hdf5("../output/fit_train_dnn.h5")
  tm_test <- system.time({label_pred <- test_dnn(fit_train, feature_test, type = "predict_classes");
   prob_pred <- test_dnn(fit_train, feature_test, type = "predict_proba")})
}

• evaluation

## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)

weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

label_test <- ifelse(label_test == 2, 1, 0)

accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)

# prob_pred <- apply(prob_pred, 1, max)
prob_pred <- prob_pred[, 2]

tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)


cat("The accuracy of model:", "DNN", "is", accu*100, "%.\n")

The accuracy of model: DNN is 76.99475 %.

cat("The AUC of model:", "DNN", "is", auc, ".\n")

The AUC of model: DNN is 0.8527232 .

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n") 
cat("Time for testing model=", tm_test[3], "s \n")

###Reference - Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.